MonkeyLearn is a text analysis platform that helps you identify and extract actionable data from a variety of raw texts, including emails, chats, webpages, papers, tweets, and more! You can use custom tags to categorize texts, such as sentiments or topics, and extract specific data, such as organizations or keywords.
AerisWeather provides a powerful weather API, mapping platform, and developer toolkits that allow weather-sensitive businesses worldwide to operate more effectively.AerisWeather Integrations
AerisWeather + Monkey LearnClassify Text in monkeylearn when New Weather Alerts is created in AerisWeather Read More...
AerisWeather + Monkey LearnExtract Text in monkeylearn when New Weather Alerts is created in AerisWeather Read More...
AerisWeather + Monkey LearnUpload training Data in monkeylearn when New Weather Alerts is created in AerisWeather Read More...
AerisWeather + Monkey LearnClassify Text in monkeylearn when New Forecast is created in AerisWeather Read More...
AerisWeather + Monkey LearnExtract Text in monkeylearn when New Forecast is created in AerisWeather Read More...
It's easy to connect Monkey Learn + AerisWeather without coding knowledge. Start creating your own business flow.
Triggers when a new forecast is created
Triggers current observation data for your location. You can select how often to get new data.
Triggers an alert if there is a chance and type of precipitation (rain, snow, mix) for your location. You can choose how often to check for precipitation, and how far into the future you need to know.
Triggers on new active weather alerts for the selected location. You can choose how often to look for new alerts.
Classifies texts with a given classifier.
Extracts information from texts with a given extractor.
Uploads data to a classifier.
Monkey Learn is a web-based Machine Learning platform that can be accessed by anyone. The website has an easy-to-navigate interface and is a fantastic resource for both people who are new to machine learning and experts. It gives users access to over 30 different algorithms, with each algorithm having unique features such as the amount of training data required, performance metrics analysis, and so on. It also provides users with the necessary code infrastructure to use their algorithms in Python, Java, and MATLAB.
AerisWeather is a weather forecasting company that predicts weather conditions at any given point of time. It has APIs that can be accessed via HTTP, XML, or SOAP protocps. The API provides data in XML format, including data regarding the current weather condition, forecasted weather conditions (up to a week in advance), and the UV index for today and the next few days. Additionally, it provides information about precipitation and sunrise/sunset times for the next 24 hours, as well as visibility and dew point data. The API can be used to integrate into various websites as well as other software applications.
To create a weather forecasting system, two key components were needed – a way to query the API to get weather data and a means to process the data into meaningful information. After some research and experimentation, it was found that the easiest way to achieve this was by using MonkeyLearn, which is a machine learning platform. In simple terms, it is a top that can be used to provide human-like reasoning capabilities to software and machines. This allows a computer to understand data as if it were a human would and make sense of it. This method was chosen because it allows instant results after data has been inputted into the system. As opposed to other methods where a large amount of data is required, this method only requires a small amount of data (such as the previous 24 hours of data. for successful results.
In order for this integration to happen, MonkeyLearn was installed on the same server as Aeris Weather’s API. As soon as the API returns an XML response from MonkeyLearn’s storage started recording all information returned, organized into separate files based on the type of information being provided (i.e., temperature, wind speed, etc. The information from each file was then passed through a pipe (a special character used in computing to pass data between programs. to a Python script that would take that information and run it through the MonkeyLearn classifier. The classifier would then analyze each piece of information provided by AerisWeather and classify them accordingly, assigning whatever label it felt was most appropriate. If the machine decided that certain information should be classified as “Cloudy” it would do so, adding another entry into a “cloudy” file in the same directory. This script then continued to output new information every so often until it had gone through all of the files belonging to AerisWeather and generated labels for each one of them. These labels were then stored in another file and written back out to disk along with the original information that was provided by AerisWeather’s API. Once complete, this newly created file contained all of the information from AerisWeather’s API with labels assigned to each piece of data and could be used in conjunction with other weather apps or programs to read the data immediately and provide meaningful information regarding the weather forecast for that day. An example of this kind of system can be seen here. https://vimeo.com/145279931 .
This integration provides an alternative means of accessing weather data than what was previously available through AerisWeather’s website. Rather than providing its users with short descriptions regarding their current conditions and forecasts, this integration gives users much more detailed information regarding their current environments. This includes things like humidity levels, wind speeds, UV index levels, cloud cover percentage, and other relevant information that would be beneficial for those who are interested in knowing more about their current conditions before leaving their home or office to go outside. Because this integration relies on machine learning, it can also provide users with very accurate predictions regarding certain weather conditions on any given day, allowing them to plan accordingly when going outside on particularly hot or cpd days or during periods of high rain fall. This integration is also cross-platform compatible and can be used on Windows machines as well as any device running one of Apple’s operating systems (MacOS or iOS.
Although the results are not always 100% accurate (as some of the factors that contribute to actual weather conditions are often difficult to predict accurately), we believe that this integration provides value to its users by giving them more detailed information about their current environment than what was previously available through the website itself.
The process to integrate Monkey Learn and AerisWeather may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.